ReviewFrontiers in psychology2022
Machine learning in biosignals processing for mental health: A narrative review.
Review in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: current status and future directions.BMC psychiatry · 2025Pooled it
- A Precision Computational Framework for sLORETA Neurofeedback in Mild Cognitive Impairment: Integration of qEEG Biomarkers and Neuropsychological Metrics.International journal of environmental research and public health · 2026Article
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- Wearable devices for anxiety assessment: a systematic review.Communications medicine · 2026Article
- Actigraphy and subjective sleep predictors of nine-year generalized anxiety disorder.Journal of anxiety disorders · 2026Article
- Predicting anorexia nervosa treatment efficacy: an explainable machine learning approach.Journal of eating disorders · 2025Article
- Pattern Identification in Patients with Functional Dyspepsia Using Brain-Body Bio-Signals: Protocol of a Clinical Trial for AI Algorithm Development.Journal of clinical medicine · 2025Article
- Advances and Challenges in Depression Marker Research.Neuropsychiatric disease and treatment · 2025Review
- Editorial: Analyzing and computing humans - the role of language, culture, brain and health.Frontiers in human neuroscience · 2024Article
- Facial Emotion Recognition in Patients with Juvenile Myoclonic Epilepsy.Journal of clinical medicine · 2023Article
- Identifying relevant asymmetry features of EEG for emotion processing.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.